{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MTL_Train_Home.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jeremy26/hydranets_course/blob/main/MTL_Train_Home.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20Fk1u9m9xfl"
      },
      "source": [
        "# Welcome to the HydraNet Home Robot Workshop 🐸🐸🐸\n",
        "\n",
        "In this workshop, you're going to learn how to train a Neural Network that does **real-time semantic segmentation and monocular depth prediction**.\n",
        "\n",
        "![](https://d3i71xaburhd42.cloudfront.net/435d4b5c30f10753d277848a17baddebd98d3c31/2-Figure1-1.png)\n",
        "\n",
        "The Model is [a Multi-Task Learning algorithm designed by Vladimir Nekrasov](https://arxiv.org/pdf/1809.04766.pdf). The entire work is based on the **DenseTorch Library**, that you can find and use [here](https://github.com/DrSleep/DenseTorch). <p>\n",
        "\n",
        "**A note —** This notebook is adapting the Library with express authorization from the author for educational purpose.\n",
        "\n",
        "## Home Robot 🤖\n",
        "* In the previous workshop of the course, you learned how to design the model shown above, and to run it on the KITTI Dataset using pretrained weights. The **KITTI Dataset only has 200 examples of segmentation**. Therefore, the authors used a technique called Knowledge Distillation and finetuned using the Cityscape dataset.<p>\n",
        "\n",
        "* 👉 In our case, we'll use another dataset called the [NYUDv2 Dataset](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html). **It contains 1449 annotated images for depth and segmentation**, which makes our life much simpler. —— Since this is an indoor dataset, we'll turn this project into a Home Robot Workshop!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ix2A28T-ZT_"
      },
      "source": [
        "#1 — Imports\n",
        "\n",
        "We're going to import:\n",
        "*   The **Data from our previous notebook** (trained model, cmaps, ...)\n",
        "*   The **NYUD Dataset**, along with helper files, ground truth examples, and train/test split files\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5QS8L_xCBLo",
        "outputId": "5dac1c18-bc52-4280-f1db-071a247869bb"
      },
      "source": [
        "!wget https://hydranets-data.s3.eu-west-3.amazonaws.com/hydranets-data-2.zip && unzip -q hydranets-data-2.zip && mv hydranets-data-2/* . && rm hydranets-data-2.zip && rm -rf hydranets-data-2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-07 13:36:26--  https://hydranets-data.s3.eu-west-3.amazonaws.com/hydranets-data-2.zip\n",
            "Resolving hydranets-data.s3.eu-west-3.amazonaws.com (hydranets-data.s3.eu-west-3.amazonaws.com)... 52.95.155.12\n",
            "Connecting to hydranets-data.s3.eu-west-3.amazonaws.com (hydranets-data.s3.eu-west-3.amazonaws.com)|52.95.155.12|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 812068227 (774M) [application/zip]\n",
            "Saving to: ‘hydranets-data-2.zip’\n",
            "\n",
            "hydranets-data-2.zi 100%[===================>] 774.45M  11.8MB/s    in 68s     \n",
            "\n",
            "2021-12-07 13:37:35 (11.4 MB/s) - ‘hydranets-data-2.zip’ saved [812068227/812068227]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szGHb1YQSyJ_"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8snxsl9eACAs"
      },
      "source": [
        "# 1 — Dataset\n",
        "Let's begin with importing our data, and visualizing it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FsAvSppK_VLY"
      },
      "source": [
        "## Load and Visualize the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjQLkkLRULyU"
      },
      "source": [
        "import glob\n",
        "\n",
        "depth = sorted(glob.glob(\"nyud/depth/*.png\"))\n",
        "seg = sorted(glob.glob(\"nyud/masks/*.png\"))\n",
        "images = sorted(glob.glob(\"nyud/rgb/*.png\"))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3kIx0CPHeJ_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f0b58ce-1d2c-46b0-d8ef-5d8b2cf412a0"
      },
      "source": [
        "print(len(images))\n",
        "print(len(depth))\n",
        "print(len(seg))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1449\n",
            "1449\n",
            "1449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XoZp3xq8jjYI"
      },
      "source": [
        "Since our dataset is a bit \"special\", we'll need a Color Map to read it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIhWdE4Hd_Ul",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f843f050-7d6d-44f3-f8e8-c39fd57c2da9"
      },
      "source": [
        "CMAP = np.load('cmap_nyud.npy')\n",
        "print(len(CMAP))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VzwxQ42D97D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "cbe826ed-2bcd-4ae9-d720-b2785de26062"
      },
      "source": [
        "idx = np.random.randint(0,len(seg))\n",
        "\n",
        "f, (ax0, ax1, ax2) = plt.subplots(1,3, figsize=(20,40))\n",
        "ax0.imshow(np.array(Image.open(images[idx])))\n",
        "ax0.set_title(\"Original\")\n",
        "ax1.imshow(np.array(Image.open(depth[idx])), cmap=\"plasma\")\n",
        "ax1.set_title(\"Depth\")\n",
        "ax2.imshow(CMAP[np.array(Image.open(seg[idx]))])\n",
        "ax2.set_title(\"Segmentation\")\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x2880 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RxtjtR_eRgt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8db18f90-9828-4d9e-e9f5-fe627992d1d2"
      },
      "source": [
        "print(np.unique(np.array(Image.open(seg[idx]))))\n",
        "print(len(np.unique(np.array(Image.open(seg[idx])))))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   1   2   5  10  12  17  20  21  24  37  38  39 255]\n",
            "14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrrBK7QY_Y_z"
      },
      "source": [
        "## Getting the DataLoader\n",
        "\n",
        "When training a model, 2 elements are going to be very important (compared to the last workshop):\n",
        "\n",
        "*   The Dataset\n",
        "*   The Training Loop, Loss, etc\n",
        "\n",
        "We already know how to design the model that does join depth and segmentation, so we only need to know how to train it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrAXPd5j63Cw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a591810-1a91-4a77-efcf-6d58d7ed57d4"
      },
      "source": [
        "data_file = \"train_list_depth.txt\"\n",
        "with open(data_file, \"rb\") as f:\n",
        "    datalist = f.readlines()\n",
        "datalist = [x.decode(\"utf-8\").strip(\"\\n\").split(\"\\t\") for x in datalist]\n",
        "root_dir = \"/nyud\"\n",
        "masks_names = (\"segm\", \"depth\")\n",
        "\n",
        "print(datalist[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['rgb/000003.png', 'masks/000003.png', 'depth/000003.png']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48VY3A057Abw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c390bb9-25e2-4c48-98f5-b156899c4d5f"
      },
      "source": [
        "import os\n",
        "abs_paths = [os.path.join(\"nyud\", rpath) for rpath in datalist[0]]\n",
        "abs_paths"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['nyud/rgb/000003.png', 'nyud/masks/000003.png', 'nyud/depth/000003.png']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ix5sznp37XdA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "25896f19-55c6-44b3-a700-3d45e7707ae5"
      },
      "source": [
        "img_arr = np.array(Image.open(abs_paths[0]))\n",
        "\n",
        "plt.imshow(img_arr)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfjiDlLr9yAs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "outputId": "8a966820-80fb-4f9c-df26-fd5e8c77d08d"
      },
      "source": [
        "masks_names = (\"segm\", \"depth\")\n",
        "\n",
        "for mask_name, mask_path in zip(masks_names, abs_paths[1:]):\n",
        "    print(mask_name)\n",
        "    print(mask_path)\n",
        "    mask = np.array(Image.open(mask_path))\n",
        "    plt.imshow(mask)\n",
        "    plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "segm\n",
            "nyud/masks/000003.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "depth\n",
            "nyud/depth/000003.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4a5e6aLuHUC"
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class HydranetDataset(Dataset):\n",
        "\n",
        "    def __init__(self, data_file, transform=None):\n",
        "        with open(data_file, \"rb\") as f:\n",
        "            datalist = f.readlines()\n",
        "        self.datalist = [x.decode(\"utf-8\").strip(\"\\n\").split(\"\\t\") for x in datalist]\n",
        "        self.root_dir = \"nyud\"\n",
        "        self.transform = transform\n",
        "        self.masks_names = (\"segm\", \"depth\")\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.datalist)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        abs_paths = [os.path.join(self.root_dir, rpath) for rpath in self.datalist[idx]] # Will output list of nyud/*/00000.png\n",
        "        sample = {}\n",
        "        sample[\"image\"] = np.array(Image.open(abs_paths[0])) #dtype = np.float32\n",
        "\n",
        "        for mask_name, mask_path in zip(self.masks_names, abs_paths[1:]):\n",
        "            mask = np.array(Image.open(mask_path))\n",
        "            assert len(mask.shape) == 2, \"Masks must be encoded without colourmap\"\n",
        "            sample[mask_name] = mask\n",
        "\n",
        "        if self.transform:\n",
        "            sample[\"names\"] = self.masks_names\n",
        "            sample = self.transform(sample)\n",
        "            # the names key can be removed by the transformation\n",
        "            if \"names\" in sample:\n",
        "                del sample[\"names\"]\n",
        "        return sample"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzR6cnx5-6oT"
      },
      "source": [
        "### Normalization — Will be common to all images\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oUOgFLww2rxt"
      },
      "source": [
        "from utils import Normalise, RandomCrop, ToTensor, RandomMirror\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuKBfbOb-5ll"
      },
      "source": [
        "img_scale = 1.0 / 255\n",
        "depth_scale = 5000.0\n",
        "\n",
        "img_mean = np.array([0.485, 0.456, 0.406])\n",
        "img_std = np.array([0.229, 0.224, 0.225])\n",
        "\n",
        "normalise_params = [img_scale, img_mean.reshape((1, 1, 3)), img_std.reshape((1, 1, 3)), depth_scale,]\n",
        "\n",
        "transform_common = [Normalise(*normalise_params), ToTensor()]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7B5LPe1_et-"
      },
      "source": [
        "### Transforms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdwYse7G_tip"
      },
      "source": [
        "crop_size = 400\n",
        "transform_train = transforms.Compose([RandomMirror(), RandomCrop(crop_size)] + transform_common)\n",
        "transform_val = transforms.Compose(transform_common)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NvjcDfO_tjr"
      },
      "source": [
        "### DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d82jskZ_vZo"
      },
      "source": [
        "train_batch_size = 4\n",
        "val_batch_size = 4\n",
        "train_file = \"train_list_depth.txt\"\n",
        "val_file = \"val_list_depth.txt\""
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQogy3tk03TP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cc71a15-c2dc-4b91-cbbe-37dd44e5a56f"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "#TRAIN DATALOADER\n",
        "trainloader = DataLoader(\n",
        "    HydranetDataset(train_file, transform=transform_train,),\n",
        "    batch_size=train_batch_size,\n",
        "    shuffle=True,\n",
        "    num_workers=4,\n",
        "    pin_memory=True,\n",
        "    drop_last=True,\n",
        ")\n",
        "\n",
        "# VALIDATION DATALOADER\n",
        "valloader = DataLoader(HydranetDataset(val_file, transform=transform_val,),\n",
        "    batch_size=val_batch_size, \n",
        "    shuffle=False, num_workers=4, \n",
        "    pin_memory=True,\n",
        "    drop_last=False,)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuR-bk-13cX6"
      },
      "source": [
        "# 2 — Creating the HydraNet\n",
        "We now have 2 DataLoaders: one for training, and one for validation/test. <p>\n",
        "\n",
        "In the next step, we're going to define our model, following the paper [Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations](https://arxiv.org/pdf/1809.04766.pdf) —— If you haven't read it yet, now is the time.\n",
        "<p>\n",
        "\n",
        "> ![](https://d3i71xaburhd42.cloudfront.net/435d4b5c30f10753d277848a17baddebd98d3c31/2-Figure1-1.png)\n",
        "\n",
        "Our model takes an input RGB image, make it go through an encoder, a lightweight refinenet decoder, and then has 2 heads, one for each task.<p>\n",
        "Things to note:\n",
        "* The only **convolutions** we'll need will be 3x3 and 1x1\n",
        "* We also need a **MaxPooling 5x5**\n",
        "* **CRP-Blocks** are implemented as Skip-Connection Operations\n",
        "* **Each Head is made of a 1x1 convolution followed by a 3x3 convolution**, only the data and the loss change there\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "025K4utrBqNy"
      },
      "source": [
        "## Building the Encoder — A MobileNetv2\n",
        "![](https://iq.opengenus.org/content/images/2020/11/conv_mobilenet_v2.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpCvdaopBGqo"
      },
      "source": [
        "def conv3x3(in_channels, out_channels, stride=1, dilation=1, groups=1, bias=False):\n",
        "    \"\"\"3x3 Convolution: Depthwise: \n",
        "    https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html\n",
        "    \"\"\"\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=bias, groups=groups)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X26XhiFLBZD5"
      },
      "source": [
        "def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False,):\n",
        "    \"1x1 Convolution: Pointwise\"\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias=bias, groups=groups)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inBcLWf3jGvd"
      },
      "source": [
        "def batchnorm(num_features):\n",
        "    \"\"\"\n",
        "    https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html\n",
        "    \"\"\"\n",
        "    return nn.BatchNorm2d(num_features, affine=True, eps=1e-5, momentum=0.1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDJDvGiQSy2L"
      },
      "source": [
        "def convbnrelu(in_channels, out_channels, kernel_size, stride=1, groups=1, act=True):\n",
        "    \"conv-batchnorm-relu\"\n",
        "    if act:\n",
        "        return nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=int(kernel_size / 2.), groups=groups, bias=False),\n",
        "                             batchnorm(out_channels),\n",
        "                             nn.ReLU6(inplace=True))\n",
        "    else:\n",
        "        return nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=int(kernel_size / 2.), groups=groups, bias=False),\n",
        "                             batchnorm(out_channels))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEXj3l0OBi1w"
      },
      "source": [
        "class InvertedResidualBlock(nn.Module):\n",
        "    \"\"\"Inverted Residual Block from https://arxiv.org/abs/1801.04381\"\"\"\n",
        "    def __init__(self, in_planes, out_planes, expansion_factor, stride=1):\n",
        "        super().__init__() # Python 3\n",
        "        intermed_planes = in_planes * expansion_factor\n",
        "        self.residual = (in_planes == out_planes) and (stride == 1) # Boolean/Condition\n",
        "        self.output = nn.Sequential(convbnrelu(in_planes, intermed_planes, 1),\n",
        "                                    convbnrelu(intermed_planes, intermed_planes, 3, stride=stride, groups=intermed_planes),\n",
        "                                    convbnrelu(intermed_planes, out_planes, 1, act=False))\n",
        "\n",
        "    def forward(self, x):\n",
        "        #residual = x\n",
        "        out = self.output(x)\n",
        "        if self.residual:\n",
        "            return (out + x)#+residual\n",
        "        else:\n",
        "            return out"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hdl3llB-85fH"
      },
      "source": [
        "class MobileNetv2(nn.Module):\n",
        "    def __init__(self, return_idx=[6]):\n",
        "        super().__init__()\n",
        "        # expansion rate, output channels, number of repeats, stride\n",
        "        self.mobilenet_config = [\n",
        "        [1, 16, 1, 1],\n",
        "        [6, 24, 2, 2],\n",
        "        [6, 32, 3, 2],\n",
        "        [6, 64, 4, 2],\n",
        "        [6, 96, 3, 1],\n",
        "        [6, 160, 3, 2],\n",
        "        [6, 320, 1, 1],\n",
        "        ]\n",
        "        self.in_channels = 32  # number of input channels\n",
        "        self.num_layers = len(self.mobilenet_config)\n",
        "        self.layer1 = convbnrelu(3, self.in_channels, kernel_size=3, stride=2)\n",
        "    \n",
        "        self.return_idx = [1, 2, 3, 4, 5, 6]\n",
        "        #self.return_idx = make_list(return_idx)\n",
        "\n",
        "        c_layer = 2\n",
        "        for t, c, n, s in self.mobilenet_config:\n",
        "            layers = []\n",
        "            for idx in range(n):\n",
        "                layers.append(InvertedResidualBlock(self.in_channels,c,expansion_factor=t,stride=s if idx == 0 else 1,))\n",
        "                self.in_channels = c\n",
        "            setattr(self, \"layer{}\".format(c_layer), nn.Sequential(*layers))\n",
        "            c_layer += 1\n",
        "\n",
        "        self._out_c = [self.mobilenet_config[idx][1] for idx in self.return_idx] # Output: [24, 32, 64, 96, 160, 320]\n",
        "\n",
        "    def forward(self, x):\n",
        "        outs = []\n",
        "        x = self.layer1(x)\n",
        "        outs.append(self.layer2(x))  # 16, x / 2\n",
        "        outs.append(self.layer3(outs[-1]))  # 24, x / 4\n",
        "        outs.append(self.layer4(outs[-1]))  # 32, x / 8\n",
        "        outs.append(self.layer5(outs[-1]))  # 64, x / 16\n",
        "        outs.append(self.layer6(outs[-1]))  # 96, x / 16\n",
        "        outs.append(self.layer7(outs[-1]))  # 160, x / 32\n",
        "        outs.append(self.layer8(outs[-1]))  # 320, x / 32\n",
        "        return [outs[idx] for idx in self.return_idx]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRmoJbmpkR0l",
        "outputId": "2ae729d3-0c80-40b1-d825-68413459a80c"
      },
      "source": [
        "encoder = MobileNetv2()\n",
        "encoder.load_state_dict(torch.load(\"mobilenetv2-e6e8dd43.pth\"))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7bg7yzbm5Cz"
      },
      "source": [
        "#print(encoder)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymQKYmnjCEng"
      },
      "source": [
        "## Building the Decoder - A Multi-Task Lighweight RefineNet\n",
        "Paper: https://arxiv.org/pdf/1810.03272.pdf\n",
        "![](https://drsleep.github.io/images/rf_arch.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Fneuzuu_7Et"
      },
      "source": [
        "def make_list(x):\n",
        "    \"\"\"Returns the given input as a list.\"\"\"\n",
        "    if isinstance(x, list):\n",
        "        return x\n",
        "    elif isinstance(x, tuple):\n",
        "        return list(x)\n",
        "    else:\n",
        "        return [x]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6Ia8Cm2BhN6"
      },
      "source": [
        "class CRPBlock(nn.Module):\n",
        "    \"\"\"CRP definition\"\"\"\n",
        "    def __init__(self, in_planes, out_planes, n_stages, groups=False):\n",
        "        super().__init__()\n",
        "        for i in range(n_stages):\n",
        "            setattr(self, '{}_{}'.format(i + 1, 'outvar_dimred'),\n",
        "                    conv1x1(in_planes if (i == 0) else out_planes,\n",
        "                            out_planes, stride=1,\n",
        "                            bias=False, groups=in_planes if groups else 1))\n",
        "        self.stride = 1\n",
        "        self.n_stages = n_stages\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        top = x\n",
        "        for i in range(self.n_stages):\n",
        "            top = self.maxpool(top)\n",
        "            top = getattr(self, '{}_{}'.format(i + 1, 'outvar_dimred'))(top)\n",
        "            x = top + x\n",
        "        return x"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNglBOFL_rPV"
      },
      "source": [
        "class MTLWRefineNet(nn.Module):\n",
        "    def __init__(self, input_sizes, num_classes, agg_size=256, n_crp=4):\n",
        "        super().__init__()\n",
        "\n",
        "        stem_convs = nn.ModuleList()\n",
        "        crp_blocks = nn.ModuleList()\n",
        "        adapt_convs = nn.ModuleList()\n",
        "        heads = nn.ModuleList()\n",
        "\n",
        "        # Reverse since we recover information from the end\n",
        "        input_sizes = list(reversed((input_sizes)))\n",
        "\n",
        "        # No reverse for collapse indices is needed\n",
        "        self.collapse_ind = [[0, 1], [2, 3], 4, 5]\n",
        "\n",
        "        groups = [False] * len(self.collapse_ind)\n",
        "        groups[-1] = True\n",
        "\n",
        "        for size in input_sizes:\n",
        "            stem_convs.append(conv1x1(size, agg_size, bias=False))\n",
        "\n",
        "        for group in groups:\n",
        "            crp_blocks.append(self._make_crp(agg_size, agg_size, n_crp, group))\n",
        "            adapt_convs.append(conv1x1(agg_size, agg_size, bias=False))\n",
        "\n",
        "        self.stem_convs = stem_convs\n",
        "        self.crp_blocks = crp_blocks\n",
        "        self.adapt_convs = adapt_convs[:-1]\n",
        "\n",
        "        num_classes = list(num_classes)\n",
        "        for n_out in num_classes:\n",
        "            heads.append(\n",
        "                nn.Sequential(\n",
        "                    conv1x1(agg_size, agg_size, groups=agg_size, bias=False),\n",
        "                    nn.ReLU6(inplace=False),\n",
        "                    conv3x3(agg_size, n_out, bias=True),\n",
        "                )\n",
        "            )\n",
        "\n",
        "        self.heads = heads\n",
        "        self.relu = nn.ReLU6(inplace=True)\n",
        "\n",
        "    def forward(self, xs):\n",
        "        xs = list(reversed(xs))\n",
        "        for idx, (conv, x) in enumerate(zip(self.stem_convs, xs)):\n",
        "            xs[idx] = conv(x)\n",
        "\n",
        "        # Collapse layers\n",
        "        c_xs = [sum([xs[idx] for idx in make_list(c_idx)]) for c_idx in self.collapse_ind ]\n",
        "\n",
        "        for idx, (crp, x) in enumerate(zip(self.crp_blocks, c_xs)):\n",
        "            if idx == 0:\n",
        "                y = self.relu(x)\n",
        "            else:\n",
        "                y = self.relu(x + y)\n",
        "            y = crp(y)\n",
        "            if idx < (len(c_xs) - 1):\n",
        "                y = self.adapt_convs[idx](y)\n",
        "                y = F.interpolate(\n",
        "                    y,\n",
        "                    size=c_xs[idx + 1].size()[2:],\n",
        "                    mode=\"bilinear\",\n",
        "                    align_corners=True,\n",
        "                )\n",
        "\n",
        "        outs = []\n",
        "        for head in self.heads:\n",
        "            outs.append(head(y))\n",
        "        return outs\n",
        "\n",
        "    @staticmethod\n",
        "    def _make_crp(in_planes, out_planes, stages, groups):\n",
        "        # Same as previous, but showing the use of a @staticmethod\n",
        "        layers = [CRPBlock(in_planes, out_planes, stages, groups)]\n",
        "        return nn.Sequential(*layers)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mf_5m6FGpXRR"
      },
      "source": [
        "num_classes = (40, 1)\n",
        "decoder = MTLWRefineNet(encoder._out_c, num_classes)\n",
        "#print(decoder)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gHOn86dCbJQ"
      },
      "source": [
        "# 3 — Train the Model\n",
        "\n",
        "Now that we've define our encoder and decoder. We are ready to train our model on the NYUDv2 Dataset.\n",
        "\n",
        "Here's what we'll need:\n",
        "\n",
        "*   Functions like **train() and valid()**\n",
        "*   **An Optimizer and a Loss Function**\n",
        "*   **Hyperparameters** such as Weight Decay, Momentum, Learning Rate, Epochs, ...\n",
        "\n",
        "Doesn't sound so bad, does it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mh3thOQ9tIH3"
      },
      "source": [
        "## Loss Function\n",
        "\n",
        "Let's begin with the Loss and Optimization we'll need.\n",
        "\n",
        "* The **Segmentation Loss** is the **Cross Entropy Loss**, working as a per-pixel classification function with 15 or so classes.\n",
        "\n",
        "* The **Depth Loss** will be the **Inverse Huber Loss**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZwy2ATBtHQf"
      },
      "source": [
        "from utils import InvHuberLoss\n",
        "\n",
        "ignore_index = 255\n",
        "ignore_depth = 0\n",
        "\n",
        "crit_segm = nn.CrossEntropyLoss(ignore_index=ignore_index).cuda()\n",
        "crit_depth = InvHuberLoss(ignore_index=ignore_depth).cuda()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Und9VjEtt73H"
      },
      "source": [
        "## Optimizer\n",
        "For the optimizer, we'll use the **Stochastic Gradient Descent**. We'll also add techniques such as weight decay or momentum."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKEiyVXGt1To"
      },
      "source": [
        "lr_encoder = 1e-2\n",
        "lr_decoder = 1e-3\n",
        "momentum_encoder = 0.9\n",
        "momentum_decoder = 0.9\n",
        "weight_decay_encoder = 1e-5\n",
        "weight_decay_decoder = 1e-5"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31Imhyw6t1az"
      },
      "source": [
        "optims = [torch.optim.SGD(encoder.parameters(), lr=lr_encoder, momentum=momentum_encoder, weight_decay=weight_decay_encoder),\n",
        "         torch.optim.SGD(decoder.parameters(), lr=lr_decoder, momentum=momentum_decoder, weight_decay=weight_decay_decoder)]"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuueL8XVvaFN"
      },
      "source": [
        "## Model Definition & State Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32DXd6vfvcIL"
      },
      "source": [
        "n_epochs = 1000"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aelXUe9wtbg"
      },
      "source": [
        "from model_helpers import Saver, load_state_dict\n",
        "import operator \n",
        "import json\n",
        "import logging\n",
        "\n",
        "init_vals = (0.0, 10000.0)\n",
        "comp_fns = [operator.gt, operator.lt]\n",
        "ckpt_dir = \"./\"\n",
        "ckpt_path = \"./checkpoint.pth.tar\"\n",
        "\n",
        "saver = Saver(\n",
        "    args=locals(),\n",
        "    ckpt_dir=ckpt_dir,\n",
        "    best_val=init_vals,\n",
        "    condition=comp_fns,\n",
        "    save_several_mode=all,\n",
        ")"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJjsbuthxPwB",
        "outputId": "b3782d08-2df4-4cc1-e491-388f83c571a9"
      },
      "source": [
        "hydranet = nn.DataParallel(nn.Sequential(encoder, decoder).cuda()) # Use .cpu() if you prefer a slow death\n",
        "\n",
        "print(\"Model has {} parameters\".format(sum([p.numel() for p in hydranet.parameters()])))\n",
        "\n",
        "start_epoch, _, state_dict = saver.maybe_load(ckpt_path=ckpt_path, keys_to_load=[\"epoch\", \"best_val\", \"state_dict\"],)\n",
        "load_state_dict(hydranet, state_dict)\n",
        "\n",
        "if start_epoch is None:\n",
        "    start_epoch = 0"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model has 3070057 parameters\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayNsDMhax-cp",
        "outputId": "954d87de-8f2d-434d-b0f8-70b5c71bd5a5"
      },
      "source": [
        "print(start_epoch)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mre_kFpoxPMA"
      },
      "source": [
        "## Learning Rate Scheduler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEze71T0wQR9"
      },
      "source": [
        "opt_scheds = []\n",
        "for opt in optims:\n",
        "    opt_scheds.append(torch.optim.lr_scheduler.MultiStepLR(opt, np.arange(start_epoch + 1, n_epochs, 100), gamma=0.1))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvPeyUELwe8e"
      },
      "source": [
        "## Training and Validation Loops\n",
        "\n",
        "Now, all we need to do is go through the Train and Validation DataLoaders, and train our model.\n",
        "\n",
        "It should look like this:\n",
        "```python\n",
        "for i in range(start_epoch, n_epochs):\n",
        "    for sched in opt_scheds:\n",
        "        sched.step(i)\n",
        "    hydranet.train() # Set to train mode    \n",
        "    train(...) # Call the train function\n",
        "\n",
        "    if i % val_every == 0:\n",
        "        model1.eval() # Set to Eval Mode\n",
        "        with torch.no_grad():\n",
        "            vals = validate(...) # Call the validate function\n",
        "```\n",
        "\n",
        "In the (...), we'll send our dataloader, loss functions, optimizers, and everything we've defined before.<p>\n",
        "\n",
        "Which means **we need a training and validate functions.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmAvw7eY0sQu"
      },
      "source": [
        "from utils import AverageMeter\n",
        "#from model_helpers import get_input_and_targets\n",
        "from tqdm import tqdm"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fI30C_Cqzc4G"
      },
      "source": [
        "def train(model, opts, crits, dataloader, loss_coeffs=(1.0,), grad_norm=0.0):\n",
        "    model.train()\n",
        "\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else \"cpu\"\n",
        "    loss_meter = AverageMeter()\n",
        "    pbar = tqdm(dataloader)\n",
        "\n",
        "    for sample in pbar:\n",
        "        loss = 0.0\n",
        "        input = sample[\"image\"].float().to(device)\n",
        "        targets = [sample[k].to(device) for k in dataloader.dataset.masks_names]\n",
        "        #[[sample[\"depth\"].to(device), sample[\"segm\"].to(device)]     \n",
        "        #input, targets = get_input_and_targets(sample=sample, dataloader=dataloader, device=device) # Get the data\n",
        "        outputs = model(input) # Forward\n",
        "        #outputs = list(outputs)\n",
        "\n",
        "        for out, target, crit, loss_coeff in zip(outputs, targets, crits, loss_coeffs):\n",
        "            loss += loss_coeff * crit(\n",
        "                F.interpolate(\n",
        "                    out, size=target.size()[1:], mode=\"bilinear\", align_corners=False\n",
        "                ).squeeze(dim=1),\n",
        "                target.squeeze(dim=1),\n",
        "            )\n",
        "\n",
        "        # Backward\n",
        "        for opt in opts:\n",
        "            opt.zero_grad()\n",
        "        loss.backward()\n",
        "        if grad_norm > 0.0:\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), grad_norm)\n",
        "        for opt in opts:\n",
        "            opt.step()\n",
        "\n",
        "        loss_meter.update(loss.item())\n",
        "        pbar.set_description(\n",
        "            \"Loss {:.3f} | Avg. Loss {:.3f}\".format(loss.item(), loss_meter.avg)\n",
        "        )"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qo5DK2vH0vcK"
      },
      "source": [
        "def validate(model, metrics, dataloader):\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else \"cpu\"\n",
        "    model.eval()\n",
        "    for metric in metrics:\n",
        "        metric.reset()\n",
        "\n",
        "    pbar = tqdm(dataloader)\n",
        "\n",
        "    def get_val(metrics):\n",
        "        results = [(m.name, m.val()) for m in metrics]\n",
        "        names, vals = list(zip(*results))\n",
        "        out = [\"{} : {:4f}\".format(name, val) for name, val in results]\n",
        "        return vals, \" | \".join(out)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for sample in pbar:\n",
        "            # Get the Data\n",
        "            input = sample[\"image\"].float().to(device)\n",
        "            targets = [sample[k].to(device) for k in dataloader.dataset.masks_names]\n",
        "\n",
        "            #input, targets = get_input_and_targets(sample=sample, dataloader=dataloader, device=device)\n",
        "            targets = [target.squeeze(dim=1).cpu().numpy() for target in targets]\n",
        "\n",
        "            # Forward\n",
        "            outputs = model(input)\n",
        "            #outputs = make_list(outputs)\n",
        "\n",
        "            # Backward\n",
        "            for out, target, metric in zip(outputs, targets, metrics):\n",
        "                metric.update(\n",
        "                    F.interpolate(out, size=target.shape[1:], mode=\"bilinear\", align_corners=False)\n",
        "                    .squeeze(dim=1)\n",
        "                    .cpu()\n",
        "                    .numpy(),\n",
        "                    target,\n",
        "                )\n",
        "            pbar.set_description(get_val(metrics)[1])\n",
        "    vals, _ = get_val(metrics)\n",
        "    print(\"----\" * 5)\n",
        "    return vals"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtPmGx3j2VKk"
      },
      "source": [
        "## Main Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FpRg81N205P"
      },
      "source": [
        "from utils import MeanIoU, RMSE"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "ozf0W-0I1yVc",
        "outputId": "33d8988d-8f71-4028-8374-c5de794297be"
      },
      "source": [
        "crop_size = 400\n",
        "batch_size = 4\n",
        "val_batch_size = 4\n",
        "val_every = 5\n",
        "loss_coeffs = (0.5, 0.5)\n",
        "\n",
        "for i in range(start_epoch, n_epochs):\n",
        "    for sched in opt_scheds:\n",
        "        sched.step(i)\n",
        "    \n",
        "    print(\"Epoch {:d}\".format(i))\n",
        "    train(hydranet, optims, [crit_segm, crit_depth], trainloader, loss_coeffs)\n",
        "    \n",
        "    if i % val_every == 0:\n",
        "        metrics = [MeanIoU(num_classes[0]),RMSE(ignore_val=ignore_depth),]\n",
        "\n",
        "        with torch.no_grad():\n",
        "            vals = validate(hydranet, metrics, valloader)\n",
        "        saver.maybe_save(new_val=vals, dict_to_save={\"state_dict\": hydranet.state_dict(), \"epoch\": i})"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
            "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/198 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "Loss 1.344 | Avg. Loss 2.079: 100%|██████████| 198/198 [00:29<00:00,  6.67it/s]\n",
            "meaniou : 0.019752 | rmse : 1.196723: 100%|██████████| 164/164 [03:11<00:00,  1.17s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "Epoch 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.576 | Avg. Loss 1.718: 100%|██████████| 198/198 [00:29<00:00,  6.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.394 | Avg. Loss 1.784: 100%|██████████| 198/198 [00:29<00:00,  6.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.631 | Avg. Loss 1.722: 100%|██████████| 198/198 [00:29<00:00,  6.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.553 | Avg. Loss 1.629: 100%|██████████| 198/198 [00:29<00:00,  6.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.445 | Avg. Loss 1.621: 100%|██████████| 198/198 [00:29<00:00,  6.75it/s]\n",
            "meaniou : 0.028260 | rmse : 1.017482: 100%|██████████| 164/164 [03:16<00:00,  1.20s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "Epoch 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss 1.742 | Avg. Loss 1.627:  80%|████████  | 159/198 [00:24<00:05,  6.51it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-ce1276ac3b02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Epoch {:d}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhydranet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcrit_segm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrit_depth\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_coeffs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mval_every\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-39-d30302e73cda>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, opts, crits, dataloader, loss_coeffs, grad_norm)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m#[[sample[\"depth\"].to(device), sample[\"segm\"].to(device)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;31m#input, targets = get_input_and_targets(sample=sample, dataloader=dataloader, device=device) # Get the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Forward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;31m#outputs = list(outputs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/parallel/data_parallel.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_ids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m             \u001b[0mreplicas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreplicas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-90f665aa61ab>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 16, x / 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 24, x / 4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 32, x / 8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 64, x / 16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer6\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 96, x / 16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-22-c22d23762986>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m#residual = x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresidual\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#+residual\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/activation.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhardtanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mhardtanh\u001b[0;34m(input, min_val, max_val, inplace)\u001b[0m\n\u001b[1;32m   1346\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhardtanh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhardtanh_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1349\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhardtanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MCe8myT4wtk"
      },
      "source": [
        "# Inference Challenge\n",
        "\n",
        "Now that your model is trained and checkpoint saved, try and **load an image from the test dataset and run your model on it**. Print the FPS.\n",
        "<p>\n",
        "\n",
        "**MEGA POINTS** — Load a video, and **implement a video pipeline** as we did on the previous workshop!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PR-brzyAzIH"
      },
      "source": [
        "#Good Luck! If you have any good result, send it to jeremy@thinkautonomous.ai directly!"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}